{"id":"https://openalex.org/W3135555536","doi":"https://doi.org/10.23919/eusipco54536.2021.9616123","title":"Online Graph Learning under Smoothness Priors","display_name":"Online Graph Learning under Smoothness Priors","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3135555536","doi":"https://doi.org/10.23919/eusipco54536.2021.9616123","mag":"3135555536"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco54536.2021.9616123","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616123","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.03762","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075498431","display_name":"Seyed Saman Saboksayr","orcid":"https://orcid.org/0000-0002-8074-2228"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Seyed Saman Saboksayr","raw_affiliation_strings":["Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","University of Rochester"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006078163","display_name":"Gonzalo Mateos","orcid":"https://orcid.org/0000-0002-9847-6298"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo Mateos","raw_affiliation_strings":["Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA","University of Rochester"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017228370","display_name":"M\u00fcjdat \u00c7etin","orcid":"https://orcid.org/0000-0002-9824-1229"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mujdat Cetin","raw_affiliation_strings":["Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA","University of Rochester"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"Goergen Institute for Data Science, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075498431"],"corresponding_institution_ids":["https://openalex.org/I5388228"],"apc_list":null,"apc_paid":null,"fwci":0.6345,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69466778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1820","last_page":"1824"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577593922615051},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6619256138801575},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5619847178459167},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5538517236709595},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4584819972515106},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.45466533303260803},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37146276235580444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3185787498950958},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09836634993553162}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577593922615051},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6619256138801575},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5619847178459167},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5538517236709595},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4584819972515106},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.45466533303260803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37146276235580444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3185787498950958},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09836634993553162}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.23919/eusipco54536.2021.9616123","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco54536.2021.9616123","pdf_url":null,"source":{"id":"https://openalex.org/S4363607854","display_name":"2021 29th European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 29th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.03762","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03762","pdf_url":"https://arxiv.org/pdf/2103.03762","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3135555536","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2103.03762","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2103.03762","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.03762","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.03762","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03762","pdf_url":"https://arxiv.org/pdf/2103.03762","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4287659018","display_name":null,"funder_award_id":"CCF-1750428,CCF-1934962,ECCS-1809356","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3135555536.pdf","grobid_xml":"https://content.openalex.org/works/W3135555536.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1684305122","https://openalex.org/W2140408177","https://openalex.org/W2486096428","https://openalex.org/W2585019672","https://openalex.org/W2593294478","https://openalex.org/W2763081248","https://openalex.org/W2787894218","https://openalex.org/W2796431263","https://openalex.org/W2798585159","https://openalex.org/W2885442657","https://openalex.org/W2913535645","https://openalex.org/W2915620785","https://openalex.org/W2959406683","https://openalex.org/W2962759781","https://openalex.org/W2963384510","https://openalex.org/W2963549694","https://openalex.org/W2964012239","https://openalex.org/W2964171990","https://openalex.org/W2994097903","https://openalex.org/W2999128473","https://openalex.org/W3028089196","https://openalex.org/W3046548275","https://openalex.org/W3084183742","https://openalex.org/W3101118888","https://openalex.org/W3118818369","https://openalex.org/W3143517296","https://openalex.org/W3169130186","https://openalex.org/W4244393449","https://openalex.org/W6680914561","https://openalex.org/W6689213722","https://openalex.org/W6746041257","https://openalex.org/W6772838925","https://openalex.org/W6780895148"],"related_works":["https://openalex.org/W3038551731","https://openalex.org/W3160209322","https://openalex.org/W3141099511","https://openalex.org/W3015065494","https://openalex.org/W3193727687","https://openalex.org/W2808733326","https://openalex.org/W3018063878","https://openalex.org/W2725485065","https://openalex.org/W3112376208","https://openalex.org/W2246700095","https://openalex.org/W2997887127","https://openalex.org/W2892189299","https://openalex.org/W2538265477","https://openalex.org/W3014807341","https://openalex.org/W3098319564","https://openalex.org/W2964760557","https://openalex.org/W3092482340","https://openalex.org/W2972765636","https://openalex.org/W2963101922","https://openalex.org/W2323658257"],"abstract_inverted_index":{"The":[0],"growing":[1],"success":[2],"of":[3,14,37,136,158],"graph":[4,16,94,100,128],"signal":[5],"processing":[6,36,93],"(GSP)":[7],"approaches":[8],"relies":[9],"heavily":[10],"on":[11,65],"prior":[12],"identification":[13],"a":[15,113,134],"over":[17],"which":[18],"network":[19,55,81,170],"data":[20,39,154],"admit":[21],"certain":[22],"regularity.":[23],"However,":[24],"adaptation":[25],"to":[26,43,62,76,111,132,164,167],"increasingly":[27],"dynamic":[28],"environments":[29],"as":[30,32],"well":[31],"demands":[33],"for":[34,53],"real-time":[35],"streaming":[38,59,165],"pose":[40],"major":[41],"challenges":[42],"this":[44,47],"end.":[45],"In":[46],"context,":[48],"we":[49,105,123],"develop":[50],"novel":[51],"algorithms":[52],"online":[54,103,127],"topology":[56,82],"inference":[57],"given":[58],"observations":[60],"assumed":[61],"be":[63],"smooth":[64],"the":[66,78,85,99,126,140,156,159],"sought":[67],"graph.":[68],"Unlike":[69],"existing":[70],"batch":[71,143],"algorithms,":[72],"our":[73],"goal":[74],"is":[75],"track":[77,168],"(possibly)":[79],"time-varying":[80,116,142],"while":[83],"maintaining":[84],"memory":[86],"and":[87,150],"computational":[88],"costs":[89],"in":[90,101,162],"check":[91],"by":[92],"signals":[95,166],"sequentially-in-time.":[96],"To":[97],"recover":[98],"an":[102],"fashion,":[104],"leverage":[106],"proximal":[107],"gradient":[108],"(PG)":[109],"methods":[110],"solve":[112],"judicious":[114],"smoothness-regularized,":[115],"optimization":[117],"problem.":[118],"Under":[119],"mild":[120],"technical":[121],"conditions,":[122],"establish":[124],"that":[125],"learning":[129],"algorithm":[130,161],"converges":[131],"within":[133],"neighborhood":[135],"(i.e.,":[137],"it":[138],"tracks)":[139],"optimal":[141],"solution.":[144],"Computer":[145],"simulations":[146],"using":[147],"both":[148],"synthetic":[149],"real":[151],"financial":[152],"market":[153],"illustrate":[155],"effectiveness":[157],"proposed":[160],"adapting":[163],"slowly-varying":[169],"connectivity.":[171]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
